Analysing uncertainties in offshore wind farm power output using measure–correlate–predict methodologies
<p>This paper investigates the uncertainties resulting from different measure–correlate–predict (MCP) methods to project the power and energy yield from a wind farm. The analysis is based on a case study that utilises short-term data acquired from a lidar wind measurement system deployed at a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-05-01
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| Series: | Wind Energy Science |
| Online Access: | https://www.wind-energ-sci.net/5/601/2020/wes-5-601-2020.pdf |
| Summary: | <p>This paper investigates the uncertainties resulting from
different measure–correlate–predict (MCP) methods to project the power and energy
yield from a wind farm. The analysis is based on a case study that utilises
short-term data acquired from a lidar wind measurement system deployed at a
coastal site in the northern part of the island of Malta and long-term
measurements from the island's international airport. The wind speed at the
candidate site is measured by means of a lidar system. The predicted power
output for a hypothetical offshore wind farm from the various MCP
methodologies is compared to the actual power output obtained directly from
the input of lidar data to establish which MCP methodology best predicts the
power generated.</p>
<p>The power output from the wind farm is predicted by inputting wind speed and
direction derived from the different MCP methods into windPRO<sup>®</sup> (<span class="uri">https://www.emd.dk/windpro</span>, last access: 8 May 2020). The predicted power is compared to
the power output generated from the actual wind and direction data by using
the normalised mean absolute error (NMAE) and the normalised mean-squared
error (NMSE). This methodology will establish which combination of MCP
methodology and wind farm configuration will have the least prediction
error.</p>
<p>The best MCP methodology which combines prediction of wind speed and wind
direction, together with the topology of the wind farm, is that using
multiple linear regression (MLR). However, the study concludes that the
other MCP methodologies cannot be discarded as it is always best to compare
different combinations of MCP methodologies for wind speed and wind
direction, together with different wake models and wind farm topologies.</p> |
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| ISSN: | 2366-7443 2366-7451 |